Evaluation of recanalisation treatment on posterior circulation ischemic stroke by Solitaire device—A multicenter retrospective study

Objectives Posterior circulation ischemic stroke (PCIS), accounting for approximately 20% of total ischemic stroke, is a sever disease that associated with high rate of morbidity and mortality. Though the effectiveness of endovascular mechanical thrombectomy has been well demonstrated in many types of ischemic stroke, it is still unclear what the outcome is in posterior circulation ischemic stroke. Methods and materials In current study, data was collected from 139 Chinese patients who received endovascular mechanical thrombectomy treatment with Solitaire device after acute posterior circulation ischemic stroke. We measured the mortality, symptomatic intracranial hemorrhage (SICH) and National Institutes of Health Stroke Scale (NIHSS) to evaluate the safety of endovascular mechanical thrombectomy. Meanwhile, the clinical outcome of endovascular mechanical thrombectomy was also evaluated based on recanalisation rate, HIHSS, and the modified Rankin Scale (mRS). Results Recanalisation was successful in 124 (89.3%) patients after surgery. Herniation was the second fatal stroke complication, out of the 6 patients suffered from herniation, 3 patients (50%) died during surgery and 2 (33%) died after surgery. As for other stroke complications such as pulmonary infection, 1 patient (4.3%) died during surgery and 1 patient (4.3%) died 3 days after surgery. Conclusion Our findings indicate that endovascular mechanical treatment is a safe treatment which brings clear benefit to patients suffered from posterior circulation ischemic stroke, in both the recanalisation rate and functional outcomes

A Deterministic Self-Organizing Map Approach and its Application on Satellite Data based Cloud Type Classification

A self-organizing map (SOM) is a type of competitive artificial neural network, which projects the high dimensional input space of the training samples into a low dimensional space with the topology relations preserved. This makes SOMs supportive of organizing and visualizing complex data sets and have been pervasively used among numerous disciplines with different applications. Notwithstanding its wide applications, the self-organizing map is perplexed by its inherent randomness, which produces dissimilar SOM patterns even when being trained on identical training samples with the same parameters every time, and thus causes usability concerns for other domain practitioners and precludes more potential users from exploring SOM based applications in a broader spectrum. Motivated by this practical concern, we propose a deterministic approach as a supplement to the standard self-organizing map. In accordance with the theoretical design, the experimental results with satellite cloud data demonstrate the effective and efficient organization as well as simplification capabilities of the proposed approach

The Construction of a Clinical Decision Support System Based on Knowledge Base

Part 7: e-Health, the New Frontier of Service Science InnovationInternational audienceBased on a review of domestic and foreign research, application status, classification, composition, and the main problem of a clinical decision support system, this paper proposed a CDSS mode based on a knowledge base. On KB-CDSS mode, this paper discussed the architecture, principle, process, construction of the knowledge base, system design, and application value, then introduced the application WanFang Data Clinical Diagnosis and Treatment Knowledge Base

Heterogeneous Trajectory Forecasting via Risk and Scene Graph Learning

Heterogeneous trajectory forecasting is critical for intelligent transportation systems, while it is challenging because of the difficulty for modeling the complex interaction relations among the heterogeneous road agents as well as their agent-environment constraint. In this work, we propose a risk and scene graph learning method for trajectory forecasting of heterogeneous road agents, which consists of a Heterogeneous Risk Graph (HRG) and a Hierarchical Scene Graph (HSG) from the aspects of agent category and their movable semantic regions. HRG groups each kind of road agents and calculates their interaction adjacency matrix based on an effective collision risk metric. HSG of driving scene is modeled by inferring the relationship between road agents and road semantic layout aligned by the road scene grammar. Based on this formulation, we can obtain an effective trajectory forecasting in driving situations, and superior performance to other state-of-the-art approaches is demonstrated by exhaustive experiments on the nuScenes, ApolloScape, and Argoverse datasets.Comment: Submitted to IEEE Transactions on Intelligent Transportation Systems, 202